Reliability Guided Resource Allocation for Large-Scale Systems

In high performance computing systems running on native hardware or cloud computing resources, parallel applications can reserve a large number of resources for long time periods. Resource failures trigger the failure of applications using this resource. Our investigation of large-scale systems in the field has revealed a difference in the operational reliability of nodes. By adding awareness of this difference to the scheduler along with the predicted reliability needs, we match reliable resources with the most demanding applications to reduce the probability of application failure. In this paper, we describe a new approach we developed to enhance reliability and reduce failure costs. Our approach partitions resources based on expected reliability and sizes each partition to bound the probability of blocking requests. Our approach can be used to size systems for peak loads with a bounded probability of blocking requests, and would be useful for operators seeking to improve the reliability and efficiency of systems.